Maxim Panov

Publication Activity (10 Years)

Years Active: 2011-2024
Publications (10 Years): 67

Top Topics

Gaussian Processes

Conformal Prediction

Top Venues

Publications

Ekaterina Fadeeva, Aleksandr Rubashevskii, Artem Shelmanov, Sergey Petrakov, Haonan Li, Hamdy Mubarak, Evgenii Tsymbalov, Gleb Kuzmin, Alexander Panchenko, Timothy Baldwin, Preslav Nakov, Maxim Panov
Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification. CoRR (2024)
Roman Vashurin, Ekaterina Fadeeva, Artem Vazhentsev, Akim Tsvigun, Daniil Vasilev, Rui Xing, Abdelrahman Boda Sadallah, Lyudmila Rvanova, Sergey Petrakov, Alexander Panchenko, Timothy Baldwin, Preslav Nakov, Maxim Panov, Artem Shelmanov
Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraph. CoRR (2024)
Ekaterina Fadeeva, Aleksandr Rubashevskii, Artem Shelmanov, Sergey Petrakov, Haonan Li, Hamdy Mubarak, Evgenii Tsymbalov, Gleb Kuzmin, Alexander Panchenko, Timothy Baldwin, Preslav Nakov, Maxim Panov
Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification. ACL (Findings) (2024)
Vincent Plassier, Nikita Kotelevskii, Aleksandr Rubashevskii, Fedor Noskov, Maksim Velikanov, Alexander Fishkov, Samuel Horváth, Martin Takác, Eric Moulines, Maxim Panov
Efficient Conformal Prediction under Data Heterogeneity. AISTATS (2024)
Nikita Kotelevskii, Maxim Panov
Predictive Uncertainty Quantification via Risk Decompositions for Strictly Proper Scoring Rules. CoRR (2024)
Maksim Velikanov, Maxim Panov, Dmitry Yarotsky
Generalization error of spectral algorithms. CoRR (2024)
Maksim Velikanov, Maxim Panov, Dmitry Yarotsky
Generalization error of spectral algorithms. ICLR (2024)
Vincent Plassier, Alexander Fishkov, Maxim Panov, Eric Moulines
Conditionally valid Probabilistic Conformal Prediction. CoRR (2024)
Artem Vazhentsev, Akim Tsvigun, Roman Vashurin, Sergey Petrakov, Daniil Vasilev, Maxim Panov, Alexander Panchenko, Artem Shelmanov
Efficient Out-of-Domain Detection for Sequence to Sequence Models. ACL (Findings) (2023)
Nikita Kotelevskii, Samuel Horváth, Karthik Nandakumar, Martin Takác, Maxim Panov
Dirichlet-based Uncertainty Quantification for Personalized Federated Learning with Improved Posterior Networks. CoRR (2023)
Fedor Noskov, Alexander Fishkov, Maxim Panov
Selective Nonparametric Regression via Testing. CoRR (2023)
Vincent Plassier, Mehdi Makni, Aleksandr Rubashevskii, Eric Moulines, Maxim Panov
Conformal Prediction for Federated Uncertainty Quantification Under Label Shift. CoRR (2023)
Gleb Kuzmin, Artem Vazhentsev, Artem Shelmanov, Xudong Han, Simon Suster, Maxim Panov, Alexander Panchenko, Timothy Baldwin
Uncertainty Estimation for Debiased Models: Does Fairness Hurt Reliability? IJCNLP (1) (2023)
Mohamed El Amine Seddik, Malik Tiomoko, Alexis Decurninge, Maxim Panov, Maxime Guillaud
Learning from Low Rank Tensor Data: A Random Tensor Theory Perspective. UAI (2023)
Artem Vazhentsev, Gleb Kuzmin, Akim Tsvigun, Alexander Panchenko, Maxim Panov, Mikhail Burtsev, Artem Shelmanov
Hybrid Uncertainty Quantification for Selective Text Classification in Ambiguous Tasks. ACL (1) (2023)
Emmanuel Akeweje, Andrey Olhin, Vsevolod Avilkin, Aleksey Vishnyakov, Maxim Panov
Real-Time Reconstruction of Complex Flow in Nanoporous Media: Linear vs Non-linear Decoding. ICCS (3) (2023)
Aleksandr Rubashevskii, Daria Kotova, Maxim Panov
Scalable Batch Acquisition for Deep Bayesian Active Learning. SDM (2023)
Fedor Noskov, Alexander Fishkov, Maxim Panov
Selective Nonparametric Regression via Testing. ACML (2023)
Akim Tsvigun, Ivan Lysenko, Danila Sedashov, Ivan Lazichny, Eldar Damirov, Vladimir Karlov, Artemy Belousov, Leonid Sanochkin, Maxim Panov, Alexander Panchenko, Mikhail Burtsev, Artem Shelmanov
Active Learning for Abstractive Text Summarization. CoRR (2023)
Ekaterina Fadeeva, Roman Vashurin, Akim Tsvigun, Artem Vazhentsev, Sergey Petrakov, Kirill Fedyanin, Daniil Vasilev, Elizaveta Goncharova, Alexander Panchenko, Maxim Panov, Timothy Baldwin, Artem Shelmanov
LM-Polygraph: Uncertainty Estimation for Language Models. CoRR (2023)
Fedor Noskov, Maxim Panov
Optimal Estimation in Mixed-Membership Stochastic Block Models. CoRR (2023)
Vincent Plassier, Nikita Kotelevskii, Aleksandr Rubashevskii, Fedor Noskov, Maksim Velikanov, Alexander Fishkov, Samuel Horváth, Martin Takác, Eric Moulines, Maxim Panov
Efficient Conformal Prediction under Data Heterogeneity. CoRR (2023)
Ekaterina Fadeeva, Roman Vashurin, Akim Tsvigun, Artem Vazhentsev, Sergey Petrakov, Kirill Fedyanin, Daniil Vasilev, Elizaveta Goncharova, Alexander Panchenko, Maxim Panov, Timothy Baldwin, Artem Shelmanov
LM-Polygraph: Uncertainty Estimation for Language Models. EMNLP (Demos) (2023)
Roman Kail, Kirill Fedyanin, Nikita Muravev, Alexey Zaytsev, Maxim Panov
ScaleFace: Uncertainty-aware Deep Metric Learning. DSAA (2023)
Vincent Plassier, Mehdi Makni, Aleksandr Rubashevskii, Eric Moulines, Maxim Panov
Conformal Prediction for Federated Uncertainty Quantification Under Label Shift. ICML (2023)
Aleksandr Rubashevskii, Daria Kotova, Maxim Panov
Scalable Batch Acquisition for Deep Bayesian Active Learning. CoRR (2023)
Alexander Fishkov, Maxim Panov
Scalable computation of prediction intervals for neural networks via matrix sketching. CoRR (2022)
Roman Kail, Kirill Fedyanin, Nikita Muravev, Alexey Zaytsev, Maxim Panov
ScaleFace: Uncertainty-aware Deep Metric Learning. CoRR (2022)
Gleb Bazhenov, Sergei Ivanov, Maxim Panov, Alexey Zaytsev, Evgeny Burnaev
Towards OOD Detection in Graph Classification from Uncertainty Estimation Perspective. CoRR (2022)
Artem Vazhentsev, Gleb Kuzmin, Artem Shelmanov, Akim Tsvigun, Evgenii Tsymbalov, Kirill Fedyanin, Maxim Panov, Alexander Panchenko, Gleb Gusev, Mikhail Burtsev, Manvel Avetisian, Leonid Zhukov
Uncertainty Estimation of Transformer Predictions for Misclassification Detection. ACL (1) (2022)
Maksim Velikanov, Roman Kail, Ivan Anokhin, Roman Vashurin, Maxim Panov, Alexey Zaytsev, Dmitry Yarotsky
Embedded Ensembles: Infinite Width Limit and Operating Regimes. CoRR (2022)
Maksim Velikanov, Roman V. Kail, Ivan Anokhin, Roman Vashurin, Maxim Panov, Alexey Zaytsev, Dmitry Yarotsky
Embedded Ensembles: infinite width limit and operating regimes. AISTATS (2022)
Akim Tsvigun, Ivan Lysenko, Danila Sedashov, Ivan Lazichny, Eldar Damirov, Vladimir Karlov, Artemy Belousov, Leonid Sanochkin, Maxim Panov, Alexander Panchenko, Mikhail Burtsev, Artem Shelmanov
Active Learning for Abstractive Text Summarization. EMNLP (Findings) (2022)
Nikita Kotelevskii, Aleksandr Artemenkov, Kirill Fedyanin, Fedor Noskov, Alexander Fishkov, Artem Shelmanov, Artem Vazhentsev, Aleksandr Petiushko, Maxim Panov
Nonparametric Uncertainty Quantification for Single Deterministic Neural Network. NeurIPS (2022)
Nikita Kotelevskii, Aleksandr Artemenkov, Kirill Fedyanin, Fedor Noskov, Alexander Fishkov, Aleksandr Petiushko, Maxim Panov
NUQ: Nonparametric Uncertainty Quantification for Deterministic Neural Networks. CoRR (2022)
Evgeny Lagutin, Daniil Selikhanovych, Achille Thin, Sergey Samsonov, Alexey Naumov, Denis Belomestny, Maxim Panov, Eric Moulines
MCMC: Sampling through Exploration Exploitation. CoRR (2021)
Konstantin F. Willeke, Paul G. Fahey, Mohammad Bashiri, Laura Hansel, Christoph Blessing, Konstantin-Klemens Lurz, Max F. Burg, Santiago A. Cadena, Zhiwei Ding, Kayla Ponder, Taliah Muhammad, Saumil S. Patel, Kaiwen Deng, Yuanfang Guan, Yiqin Zhu, Kaiwen Xiao, Xiao Han, Simone Azeglio, Ulisse Ferrari, Peter Neri, Olivier Marre, Adrian Hoffmann, Kirill Fedyanin, Kirill Vishniakov, Maxim Panov, Subash Prakash, Kishan Naik, Kantharaju Narayanappa, Alexander S. Ecker, Andreas S. Tolias, Fabian H. Sinz
Retrospective on the SENSORIUM 2022 competition. NeurIPS (Competition and Demos) (2021)
Achille Thin, Nikita Kotelevskii, Arnaud Doucet, Alain Durmus, Eric Moulines, Maxim Panov
Monte Carlo Variational Auto-Encoders. CoRR (2021)
Kirill Fedyanin, Evgenii Tsymbalov, Maxim Panov
Dropout Strikes Back: Improved Uncertainty Estimation via Diversity Sampling. AIST (Supplement) (2021)
Achille Thin, Nikita Kotelevskii, Arnaud Doucet, Alain Durmus, Eric Moulines, Maxim Panov
Monte Carlo Variational Auto-Encoders. ICML (2021)
Artem Shelmanov, Evgenii Tsymbalov, Dmitry Puzyrev, Kirill Fedyanin, Alexander Panchenko, Maxim Panov
How Certain is Your Transformer? EACL (2021)
Valentina Shumovskaia, Kirill Fedyanin, Ivan Sukharev, Dmitry Berestnev, Maxim Panov
Linking bank clients using graph neural networks powered by rich transactional data. Int. J. Data Sci. Anal. 12 (2) (2021)
Alexander Fishkov, Maxim Panov
Scalable Computation of Prediction Intervals for Neural Networks via Matrix Sketching. AIST (2021)
Aleksandr Artemenkov, Maxim Panov
NCVis: Noise Contrastive Approach for Scalable Visualization. WWW (2020)
Valentina Shumovskaia, Kirill Fedyanin, Ivan Sukharev, Dmitry Berestnev, Maxim Panov
Linking Bank Clients using Graph Neural Networks Powered by Rich Transactional Data. CoRR (2020)
Ivan Sukharev, Valentina Shumovskaia, Kirill Fedyanin, Maxim Panov, Dmitry Berestnev
EWS-GCN: Edge Weight-Shared Graph Convolutional Network for Transactional Banking Data. ICDM (2020)
Valentina Shumovskaia, Kirill Fedyanin, Ivan Sukharev, Dmitry Berestnev, Maxim Panov
Linking Bank Clients using Graph Neural Networks Powered by Rich Transactional Data: Extended Abstract. DSAA (2020)
Achille Thin, Nikita Kotelevskii, Jean-Stanislas Denain, Leo Grinsztajn, Alain Durmus, Maxim Panov, Eric Moulines
MetFlow: A New Efficient Method for Bridging the Gap between Markov Chain Monte Carlo and Variational Inference. CoRR (2020)
Ivan Sukharev, Valentina Shumovskaia, Kirill Fedyanin, Maxim Panov, Dmitry Berestnev
EWS-GCN: Edge Weight-Shared Graph Convolutional Network for Transactional Banking Data. CoRR (2020)
Evgenii Tsymbalov, Kirill Fedyanin, Maxim Panov
Dropout Strikes Back: Improved Uncertainty Estimation via Diversity Sampled Implicit Ensembles. CoRR (2020)
Aleksandr Artemenkov, Maxim Panov
NCVis: Noise Contrastive Approach for Scalable Visualization. CoRR (2020)
Evgenii Tsymbalov, Sergei Makarychev, Alexander Shapeev, Maxim Panov
Deeper Connections between Neural Networks and Gaussian Processes Speed-up Active Learning. IJCAI (2019)
Marina Gomtsyan, Nikita Mokrov, Maxim Panov, Yury Yanovich
Geometry-Aware Maximum Likelihood Estimation of Intrinsic Dimension. ACML (2019)
Alexander Menshchikov, Dmitry Ermilov, I. Dranitsky, L. Kupchenko, Maxim Panov, Maxim V. Fedorov, Andrey Somov
Data-Driven Body-Machine Interface for Drone Intuitive Control through Voice and Gestures. IECON (2019)
Marina Gomtsyan, Nikita Mokrov, Maxim Panov, Yury Yanovich
Geometry-Aware Maximum Likelihood Estimation of Intrinsic Dimension. CoRR (2019)
Evgenii Tsymbalov, Sergei Makarychev, Alexander Shapeev, Maxim Panov
Deeper Connections between Neural Networks and Gaussian Processes Speed-up Active Learning. CoRR (2019)
Evgenii Tsymbalov, Maxim Panov, Alexander Shapeev
Dropout-based Active Learning for Regression. CoRR (2018)
Evgenii Tsymbalov, Maxim Panov, Alexander Shapeev
Dropout-Based Active Learning for Regression. AIST (2018)
Ivan Nazarov, Boris Shirokikh, Maria Burkina, Gennady Fedonin, Maxim Panov
Sparse Group Inductive Matrix Completion. CoRR (2018)
Maxim Panov, Stanislav Tsepa
Constructing Graph Node Embeddings via Discrimination of Similarity Distributions. ICDM Workshops (2018)
Stanislav Tsepa, Maxim Panov
Constructing Graph Node Embeddings via Discrimination of Similarity Distributions. CoRR (2018)
Nikita Mokrov, Maxim Panov, Boris A. Gutman, Joshua I. Faskowitz, Neda Jahanshad, Paul M. Thompson
Simultaneous Matrix Diagonalization for Structural Brain Networks Classification. COMPLEX NETWORKS (2017)
Maxim Panov, Konstantin Slavnov, Roman Ushakov
Consistent Estimation of Mixed Memberships with Successive Projections. COMPLEX NETWORKS (2017)
Dmitry Ermilov, Maxim Panov, Yury Yanovich
Automatic Bitcoin Address Clustering. ICMLA (2017)
Mikhail Belyaev, Evgeny Burnaev, Ermek Kapushev, Maxim Panov, Pavel V. Prikhodko, Dmitry P. Vetrov, Dmitry Yarotsky
GTApprox: Surrogate modeling for industrial design. Adv. Eng. Softw. 102 (2016)
Mikhail Belyaev, Evgeny Burnaev, Ermek Kapushev, Maxim Panov, Pavel V. Prikhodko, Dmitry P. Vetrov, Dmitry Yarotsky
GTApprox: surrogate modeling for industrial design. CoRR (2016)
Konstantin Slavnov, Maxim Panov
Overlapping Community Detection in Weighted Graphs: Matrix Factorization Approach. IDP (2016)
Evgeny Burnaev, Maxim Panov
Adaptive Design of Experiments Based on Gaussian Processes. SLDS (2015)
Maxim Panov, Alexander Tatarchuk, Vadim Mottl, David Windridge
A Modified Neutral Point Method for Kernel-Based Fusion of Pattern-Recognition Modalities with Incomplete Data Sets. MCS (2011)